Power Systems Stability Control : Reinforcement Learning Framework

Abstract: In this paper we explore how a computational approach to learning from
interactions, called Reinforcement Learning (RL), can be applied to
control power systems. We describe some challenges in power system
control and discuss how some of those challenges could be met by using
these RL methods. The difficulties associated with their application
to control power systems are described and discussed as well as
strategies that can be adopted to overcome them. Two reinforcement
learning modes are considered : the on-line mode in which the
interaction occurs with the real power system and the off-line mode in
which the interaction occurs with a simulation model of the real power
system. We present two case studies made on a 4-machine power system
model. The first one concerns the design by means of RL algorithms
used in off-line mode of a dynamic brake controller. The second
concerns RL methods used in on-line mode when applied to control a
Thyristor Controlled Series Capacitor (TCSC) aimed to damp power
system oscillations.